/// <summary>
/// Construct a BackpropagationLayer object that corresponds to a specific neuron layer.
/// </summary>
/// <param name="backpropagation">The back propagation training object.</param>
/// <param name="layer">The layer that this object corresponds to.</param>
public BackpropagationLayer(Backpropagation backpropagation,
FeedforwardLayer layer)
{
this.backpropagation = backpropagation;
this.layer = layer;
int neuronCount = layer.NeuronCount;
this.error = new double[neuronCount];
this.errorDelta = new double[neuronCount];
if (layer.Next != null)
{
this.accMatrixDelta = new Matrix.Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
this.matrixDelta = new Matrix.Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
this.biasRow = layer.NeuronCount;
}
}
/// <summary>
/// Construct a BackpropagationLayer object that corresponds to a specific neuron layer.
/// </summary>
/// <param name="backpropagation">The back propagation training object.</param>
/// <param name="layer">The layer that this object corresponds to.</param>
public BackpropagationLayer(Backpropagation backpropagation,
FeedforwardLayer layer)
{
this.backpropagation = backpropagation;
this.layer = layer;
int neuronCount = layer.NeuronCount;
this.error = new double[neuronCount];
this.errorDelta = new double[neuronCount];
if (layer.Next != null)
{
this.accMatrixDelta = new Matrix.Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
this.matrixDelta = new Matrix.Matrix(layer.NeuronCount + 1, layer.Next.NeuronCount);
this.biasRow = layer.NeuronCount;
}
}
/// <summary>
/// Method that is called to start the incremental prune process.
/// </summary>
public void StartIncremental()
{
this.hiddenNeuronCount = 1;
this.cycles = 0;
this.done = false;
this.currentNetwork = new FeedforwardNetwork();
this.currentNetwork
.AddLayer(new FeedforwardLayer(this.train[0].Length));
this.currentNetwork.AddLayer(new FeedforwardLayer(
this.hiddenNeuronCount));
this.currentNetwork
.AddLayer(new FeedforwardLayer(this.ideal[0].Length));
this.currentNetwork.Reset();
this.backprop = new Backpropagation(this.currentNetwork, this.train,
this.ideal, this.rate, this.momentum);
}
/// <summary>
/// Internal method that is called at the end of each incremental cycle.
/// </summary>
protected void Increment()
{
bool doit = false;
if (this.markErrorRate == 0)
{
this.markErrorRate = this.error;
this.sinceMark = 0;
}
else
{
this.sinceMark++;
if (this.sinceMark > 10000)
{
if ((this.markErrorRate - this.error) < 0.01)
{
doit = true;
}
this.markErrorRate = this.error;
this.sinceMark = 0;
}
}
if (this.error < this.maxError)
{
this.done = true;
}
if (doit)
{
this.cycles = 0;
this.hiddenNeuronCount++;
this.currentNetwork = new FeedforwardNetwork();
this.currentNetwork.AddLayer(new FeedforwardLayer(
this.train[0].Length));
this.currentNetwork.AddLayer(new FeedforwardLayer(
this.hiddenNeuronCount));
this.currentNetwork.AddLayer(new FeedforwardLayer(
this.ideal[0].Length));
this.currentNetwork.Reset();
this.backprop = new Backpropagation(this.currentNetwork,
this.train, this.ideal, this.rate, this.momentum);
}
}
